Business use of computers and IT

1 Main financial systems

• Purchases and sales invoicing: records orders, goods received or dispatched, supplier invoices, customer invoices and amounts payable or receivable.

• Payroll: records employee details, time or output, gross pay, deductions and net pay, and produces payslips and payment information.

• Credit control: sets credit limits, monitors customer balances and overdue debts, and supports collection action.

• Cash and working capital management: monitors cash receipts and payments, bank balances, inventories, receivables and payables so that liquidity is maintained.Spreadsheets

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You have almost certainly used a spreadsheet, such as Excel.

The program presents you with a grid and each cell can be referenced by its column and row. Each cell can contain:

• A number, or
  

• Text or
  

• A formula
  

The formula can reference other cells so that if cell A2 contained 5 and A3 contained 9, if you typed into cell A4:

=A2 + A3

it would show 14. If A2 were changed to 10, then A4 would immediately change to 19.

Spreadsheets are immensely useful for creating budgets and cash flows. Usually, the amounts to put into these documents are not certain and spreadsheets allow assumptions to be changed and the budget is immediately recalculated.

Graphs and charts can be created by the spreadsheet by defining which cells are to be shown on the diagram. Charts and graphs can often be more informative and memorable than simple columns of figures.

2 Databases

We probably understand that databases hold large amounts of data. They do, but their key characteristic is that many people can share the data for their own purposes. So an employee database would allow the wages department to calculate wages, the personnel department to see who had a certain qualification and the costing department to see how much an hour of someone’s time would cost.

Each piece of data is held only once. This means that it only has to be updated once and also that every user sees the same data. There will be no timing differences where two versions of the records might show different information.

Because there is such a huge and varied amount of data, the data must be safeguarded and access to the database must be strictly controlled so that users cannot access data that is none of their business and that data cannot be incorrectly changed - otherwise a wide variety of users is likely to be using wrong information.

Typically, a database will consist of a number of related computer files. However, files are usually constructed as follows:

Each file consists of records. Each record is for an entire such as a customer, a supplier or an employee.

Each record consists of fields or attributes. So an employee file would have attributes of: employee number, name, salary, tax code, department, date joining….etc. One attribute is unique to each record so that the record can be clearly identified. In the employee example, employee number would be the normal unique attribute (or key field).

Each attribute is made up of characters. So if the name attribute were ‘J Smith’, each letter of the name is a character.

One data has been entered into the database is can be easily retrieved using a structured query language (or SQL). So:

You will easily appreciate how useful it can be to search and retrieve information relating to specific entries - for example in a customer database finding all customers who have bought goods worth more than $500,000.

3 Big data

There are many definition the term ‘big data’ but most suggest something like the following:

“Extremely large collections of data (data sets) that may be analysed to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.”

In 2001 Doug Laney, an analyst with Gartner (a large US IT consultancy company) stated that big data has the following characteristics, known as the 3Vs:

• Volume: a very large amount of data. More than can be easily handled by a single computer, spreadsheet or conventional database system.
  

• Variety: disparate non-uniform data of different sizes, sources, shape, arriving irregularly, some from internal sources and some from external sources, some structured, but much of it is unstructured.
  

• Velocity: data arrives continually and often has to be processed very quickly to yield useful results.
  

Sometimes a fourth ‘V’ is added: veracity: Is the data true? Can its accuracy be relied upon?

Think about the volume, variety and velocity of data that the following applications deal with:

• Retailers:
  

  • Via loyalty cards being swiped at checkouts: details of all purchases you make, when, where, how you pay, use of coupons.
    

  • Via websites: every product you have every looked at, every page you have visited, every product you have ever bought. (To paraphrase a Sting song “Every click you make I’ll be watching you”.)
    

• Social media (such as Facebook and Twitter):
  Friends and contacts, postings made, your location when postings are made, photographs (that can be scanned for identification), any other data you might choose to reveal to the universe.
  

• Mobile phone companies:        
  Numbers you ring, texts you send (which can be automatically scanned for key words), every location your phone has ever been whilst switched on (to an accuracy of a few metres), your browsing habits. Voice mails.
  

• Internet providers and browser providers:        
  Every site and every page you visit. Information about all downloads, searches and all emails (again these are routinely scanned to provide insights into your interests). Search terms you enter.
  

• Banking systems:        
  Every receipt, payment, credit card payment information (amount, date, retailer, location), location of ATM machines used.
  

This data can be both structured and unstructured:

• Structured data: this data is stored within defined fields (numerical, text, date etc) often with defined lengths, within a defined record, in a file of similar records.
  

• Unstructured data: refers to information that does not have a pre-defined data-model. It comes in all shapes and sizes and this variety and irregularities make it difficult to store it in a way that will allow it to be analysed, searched or otherwise used.
  

Clusters of computers are needed to hold the vast volume of information. Single servers are of little use and thousands of machines are needed each offering local computation and storage.

The processing of big data is generally known as big data analytics and includes:

• Data mining: analysing data to identify patterns and establish relationships such as associations (where several events are connected), sequences (where one event leads to another) and correlations.
  

• Predictive analytics: a type of data mining which aims to predict future events. For example, the chance of someone being persuaded to upgrade a flight.
  

• Text analytics: scanning text such as emails and word processing documents to extract useful information. It could simply be looking for key-words that indicate an interest in a product or place.
  

• Voice analytics: as above with audio.
  

• Statistical analytics: used to identify trends, correlations and changes in behaviour.
  

Google provides web-site owners with Google Analytics that will track many features of web-site traffic.

Obviously, organisations which amass big data have to be very careful to hold it securely and confidentially. Leaks of data are expensive both in terms of reputational damage to the company, damages claimed by victims and fines imposed by regulators.

4 Artificial intelligence

Artificial intelligence (AI) is a general term that uses to a number of technologies to make machines ‘smart’. It can use techniques such as machine learning, image recognition, speech recognition and deep learning, which uses networks capable of learning unsupervised from data that is unstructured.

One of the recent great successes of AI was a program called AlphaGo, developed by DeepMind, a subsidiary of Google. The program was taught the rules of the game Go, then it proceeded to play thousands of games against itself, gradually learning winning strategies. No human was involved in coaching the program, yet after a relatively short time it became world-beating and, in fact, discovered a number of innovative and unexpected winning moves.

Many organizations are now investing in AI research and applications to automate processes and to enhance, support or replicate human intelligence. The applications are particularly relevant to analytical and decision-making tasks.

AI can have great relevance to auditing. For example, a good auditor might know almost instinctively that a set of financial statements ‘looks wrong’. Or the auditor might identify inconsistencies between different amounts. For example, if average borrowings have increased it would be reasonable to expect interest payments to increase also. AI opens the possibility that machines could learn to identify potential problems then alert human auditors who could investigate further.

Artificial Intelligence has also been used successfully in fraud detection. AI can learn which account movements are normal (for example, that a payment happens regularly each month) and therefore detect irregularities and predict possible fraudulent behaviour. It will then automatically alert users so that further investigation can be carried out.

Artificial Intelligence can also be used to create predictive models to forecast revenues and cash flow. For example, AI can analyse large amounts of data better than humans and can detect very complex or subtle patterns. This allows them to make more precise predictions in more complicated environments and, vital to the idea of learning, analyse why sometimes the predictions are wrong so that next time results should be more accurate.

5 Accounting packages

Accounting packages maintain the business accounts, allowing the debits and credits to be easily recorded. They will always ensure that debits = credits, and that the trial balance will therefore always balance.

Once the debits and credits have been recorded, accounting packages then allow a series of reports to be produced quickly. For example:

• Profit and loss account
  

• Statement of financial position
  

• Aged receivable analysis
  

• Lists of amounts owing from customers/owing to suppliers
  

• Inventory movements
  

• Invoice production
  

• Comparison of actual and budgeted results (if budgets had been set up), comparison of this period to last period etc.
  

Accounting packages allow accounting information to be:

• Recorded more quickly
  

• Recorded more accurately
  

• Analysed and used more quickly and creatively.
  

• Recorded and used more economically (fewer employees are needed).
  

Packages can be a simple or as complex as the business needs and now, even very small businesses are unlikely yo use manual recording methods.

6 Cloud computing

In traditional computer networks each user has a computer know as ‘a client’ and documents, spreadsheets etc would be stored centrally on a machine called ‘a server’. When a document is being worked on it is copied from the server to the client machine where it is operated on by Word, Excel etc. It is then saved back to the server.

The advantage of this is that the central storage of documents means that they can be used and shared by everyone on the network. A major disadvantage is that every client machine has to have a copy of the programs: Word, Excel etc. Not only is this expensive, it can cause major problems if the software has to be updated as every copy has to be changed. Inevitably, different machines end up with different software versions.

Another problem is that client machines have to be powerful enough to run every application they might need to, even if powerful computing were needed only occasionally (such as by graphics-intensive programs).

With cloud computing there is only one copy of each program and this is held on the server within a web-based interface. Users log onto the system and processing is then carried out on the server or ‘cloud’ of servers. It appears to each user that they are operating a local version of the software but the processing is actually performed on the server. As more computer power is needed more cloud resources can be used. This gives great flexibility.

Client machines can be ‘thin clients’, meaning that they are not powerful. Hardware, software and maintenance costs are greatly reduced, though the system is more vulnerable to disruption as continuous links are needed to the server. For example, relatively small machines, such as laptops, can be used for computation intensive graphics operations because the processing is performed on the server and the operator’s commands and computer results are shown on the local laptop.

Hotmail and Gmail are examples of cloud processing. Whenever you log on and write an email, the processing is carried out by the system’s cloud - not on your local computer. All your computer has to do is to handle the interface.

7 Blockchain technology and distributed ledgers

Blockchain technology allows data to be added to a set of records, but once added it cannot be changed without detection.

When a piece of data is added (a ‘block’) a hash amount is calculated by a complex mathematical process. When another block is added (like adding a link in a chain, hence ‘blockchain’) the hash of the previous block is also included with the new block and a hash of the whole new block produced.

Any alteration of data in, say the first block, will change its hash, but that will then not agree with what is recorded in the second block, and so on down the chain. The integrity of the records has been interfered with and will lead to internal inconsistency that could only be rectified by huge computational power. New blocks can be added, but previous ones cannot be amended or removed without detection.

For example, information in each block could be dealing with ownership, taxes paid and planning permissions given for a piece of land so that the chain of blocks will display everything about the land in a way that cannot be changed, but which can be extended.

Another feature of blockchain technology is that the information is held simultaneously in many computers on a network and the copies of the information are continually compared and validated. This distributed processing effect, known as a distributed ledger, means that the blockchain is secure from attack. If one computer is damaged, there are many copies still left on the network.

Cryptocurrencies, such as Bitcoin and Ethereum, make use of blockchain technology to record transaction and ownership. The following is a simplified explanation.

1. Think of a bitcoin as especially constructed number. It takes enormous computing power to produce a valid number.
   

2. When you acquire a bitcoin your record of ownership is entered into the bitcoin’s blockchain and you are given a key (like a password) that will let you carry out transactions with the bitcoin.
   

3. If you pay someone using the bitcoin, the blockchain is updated with information about that transaction by adding another block. It will note you do not own it and that someone else does and the date and time of the transfer is noted too.
   

To update the blockchain you have to use the key, so that keeps the transaction secure. You will not be able to spend that bitcoin because the blockchain has recorded that you are no longer the owner. The blockchain is itself secure because of the use of hashing and the distributed recording of the chain. Note that transfers of cryptocurrencies and software value tokens do not require an intermediary such as a bank. This makes transaction faster and cheaper. Effectively, they cut out the intermediaries.

8 Internal and external information

Information used by business is obtained form both internal and external sources. Here are some examples of internal and external information. Be aware of the very wide variety of information and information sources that might be useful to organisations.

• Internal sources
  

  • Sales analyses
    

  • Inventory reports
    

  • Debtor ageing
    

  • Attendance records
    

• External sources
  

  • Internet
    

  • Marketing literature
    

  • Conferences and courses
    

  • Commercial databases
    

  • Government